Subclass-based multi-task learning for Alzheimer's disease diagnosis

Frontiers in Aging Neuroscience

Volumn 6, Issue JUN, 2014, Pages 1-20

  Suk, Heung Il ^a   Shen, Dinggang ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

Alzheimer's disease; Feature selection; K means clustering; Mild cognitive impairment; Neuroimaging analysis

Indexed keywords

AGED; ALZHEIMER DISEASE; ARTICLE; CLUSTER ANALYSIS; CONTROLLED STUDY; DISEASE CLASSIFICATION; EARLY DIAGNOSIS; FEMALE; HUMAN; IMAGE PROCESSING; LEARNING; MAJOR CLINICAL STUDY; MALE; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; NORMAL DISTRIBUTION; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; SUPPORT VECTOR MACHINE; TASK PERFORMANCE; CEREBROSPINAL FLUID ANALYSIS; CLINICAL DEMENTIA RATING; COMPARATIVE EFFECTIVENESS; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE COURSE; KERNEL METHOD; MACHINE LEARNING; MINI MENTAL STATE EXAMINATION; NUCLEAR MAGNETIC RESONANCE SCANNER; RESPONSE VARIABLE; SENSITIVITY AND SPECIFICITY; WECHSLER MEMORY SCALE;

AMYLOID BETA PROTEIN[1-42]; FLUORODEOXYGLUCOSE F 18; TAU PROTEIN;

EID: 84904556055     PISSN: None     EISSN: 16634365     Source Type: Journal    
DOI: 10.3389/fnagi.2014.00168     Document Type: Article

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